Update app.py

app.py

@@ -1,12 +1,201 @@
+# This cell will generate a unified Gradio app.py content based on all 5 apps provided
+
+import os
 import gradio as gr
+import pandas as pd
+import numpy as np
+import joblib
+import spacy
+from transformers import pipeline
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain.prompts import HumanMessagePromptTemplate, ChatPromptTemplate
+from langchain.output_parsers import PydanticOutputParser
+from langchain_openai import ChatOpenAI
+
+# ---------------- Text Translator ---------------- #
+
+chat = ChatOpenAI()
+
+class TextTranslator(BaseModel):
+    output: str = Field(description="Translated output text")
+
+output_parser = PydanticOutputParser(pydantic_object=TextTranslator)
+format_instructions = output_parser.get_format_instructions()
+
+def text_translator(input_text: str, language: str) -> str:
+    human_template = f"Enter the text that you want to translate: {{input_text}}, and enter the language that you want it to translate to {{language}}. {format_instructions}"
+    human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+    chat_prompt = ChatPromptTemplate.from_messages([human_message_prompt])
+    prompt = chat_prompt.format_prompt(input_text=input_text, language=language, format_instructions=format_instructions)
+    messages = prompt.to_messages()
+    response = chat(messages=messages)
+    output = output_parser.parse(response.content)
+    return output.output
+
+# ---------------- Sentiment Analysis ---------------- #
+
+sentiment_classifier = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment")
+
+def sentiment_analysis(message, history):
+    result = sentiment_classifier(message)
+    return f"Sentiment: {result[0]['label']} (Probability: {result[0]['score']:.2f})"
+
+# ---------------- Financial Analyst ---------------- #
+
+nlp = spacy.load('en_core_web_sm')
+nlp.add_pipe('sentencizer')
+
+def split_in_sentences(text):
+    doc = nlp(text)
+    return [str(sent).strip() for sent in doc.sents]
+
+def make_spans(text, results):
+    results_list = [res['label'] for res in results]
+    return list(zip(split_in_sentences(text), results_list))
+
+auth_token = os.environ.get("HF_Token")
+
+asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
+def speech_to_text(speech):
+    return asr(speech)["text"]
+
+summarizer = pipeline("summarization", model="knkarthick/MEETING_SUMMARY")
+def summarize_text(text):
+    return summarizer(text)[0]['summary_text']
+
+fin_model = pipeline("sentiment-analysis", model='yiyanghkust/finbert-tone', tokenizer='yiyanghkust/finbert-tone')
+def text_to_sentiment(text):
+    return fin_model(text)[0]["label"]
+
+def fin_ner(text):
+    api = gr.Interface.load("dslim/bert-base-NER", src='models', use_auth_token=auth_token)
+    return api(text)
+
+def fin_ext(text):
+    results = fin_model(split_in_sentences(text))
+    return make_spans(text, results)
+
+def fls(text):
+    fls_model = pipeline("text-classification", model="demo-org/finbert_fls", tokenizer="demo-org/finbert_fls", use_auth_token=auth_token)
+    results = fls_model(split_in_sentences(text))
+    return make_spans(text, results)
+
+# ---------------- Personal Information Identifier ---------------- #
+
+def detect_personal_info(text):
+    pii_model = gr.Interface.load("models/iiiorg/piiranha-v1-detect-personal-information")
+    return pii_model(text)
+
+# ---------------- Customer Churn ---------------- #
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+pipeline_path = os.path.join(script_dir, 'toolkit', 'pipeline.joblib')
+model_path = os.path.join(script_dir, 'toolkit', 'Random Forest Classifier.joblib')
+
+pipeline_churn = joblib.load(pipeline_path)
+model_churn = joblib.load(model_path)
+
+def calculate_total_charges(tenure, monthly_charges):
+    return tenure * monthly_charges
+
+def predict_churn(SeniorCitizen, Partner, Dependents, tenure,
+    InternetService, OnlineSecurity, OnlineBackup, DeviceProtection, TechSupport,
+    StreamingTV, StreamingMovies, Contract, PaperlessBilling, PaymentMethod,
+    MonthlyCharges):
+
+    TotalCharges = calculate_total_charges(tenure, MonthlyCharges)
+    input_df = pd.DataFrame({
+        'SeniorCitizen': [SeniorCitizen],
+        'Partner': [Partner],
+        'Dependents': [Dependents],
+        'tenure': [tenure],
+        'InternetService': [InternetService],
+        'OnlineSecurity': [OnlineSecurity],
+        'OnlineBackup': [OnlineBackup],
+        'DeviceProtection': [DeviceProtection],
+        'TechSupport': [TechSupport],
+        'StreamingTV': [StreamingTV],
+        'StreamingMovies': [StreamingMovies],
+        'Contract': [Contract],
+        'PaperlessBilling': [PaperlessBilling],
+        'PaymentMethod': [PaymentMethod],
+        'MonthlyCharges': [MonthlyCharges],
+        'TotalCharges': [TotalCharges]
+    })
+
+    cat_cols = [col for col in input_df.columns if input_df[col].dtype == 'object']
+    num_cols = [col for col in input_df.columns if input_df[col].dtype != 'object']
+
+    X_processed = pipeline_churn.transform(input_df)
+
+    cat_encoder = pipeline_churn.named_steps['preprocessor'].named_transformers_['cat'].named_steps['onehot']
+    cat_feature_names = cat_encoder.get_feature_names_out(cat_cols)
+    feature_names = num_cols + list(cat_feature_names)
+    final_df = pd.DataFrame(X_processed, columns=feature_names)
+
+    first_three_columns = final_df.iloc[:, :3]
+    remaining_columns = final_df.iloc[:, 3:]
+    final_df = pd.concat([remaining_columns, first_three_columns], axis=1)
+
+    prediction_probs = model_churn.predict_proba(final_df)[0]
+    return {
+        "Prediction: CHURN 🔴": prediction_probs[1],
+        "Prediction: STAY ✅": prediction_probs[0]
+    }
+
+# ---------------- Interface ---------------- #
 
-# สร้างแอปที่มีสองแท็บ
 with gr.Blocks() as app:
-    with gr.Tab("Tab 1"):
-        gr.Markdown("This is Tab 1")  # ข้อความในแท็บแรก
-    
-    with gr.Tab("Tab 2"):
-        gr.Markdown("This is Tab 2")  # ข้อความในแท็บที่สอง
+    with gr.Tab("Text Translator"):
+        input_text = gr.Textbox(label="Enter text to translate")
+        lang = gr.Textbox(label="Target language (e.g., Hindi, French)")
+        output_text = gr.Textbox(label="Translated text")
+        gr.Button("Translate").click(fn=text_translator, inputs=[input_text, lang], outputs=output_text)
+
+    with gr.Tab("Sentiment Analysis"):
+        gr.ChatInterface(sentiment_analysis)
+
+    with gr.Tab("Financial Analyst"):
+        audio_input = gr.Audio(source="microphone", type="filepath")
+        text = gr.Textbox(label="Transcribed Text")
+        gr.Button("Transcribe").click(fn=speech_to_text, inputs=audio_input, outputs=text)
+        stext = gr.Textbox(label="Summary")
+        gr.Button("Summarize").click(fn=summarize_text, inputs=text, outputs=stext)
+        gr.Button("Financial Tone").click(fn=text_to_sentiment, inputs=stext, outputs=gr.Label())
+        gr.Button("NER").click(fn=fin_ner, inputs=text, outputs=gr.HighlightedText())
+        gr.Button("Tone per sentence").click(fn=fin_ext, inputs=text, outputs=gr.HighlightedText())
+        gr.Button("Forward-looking").click(fn=fls, inputs=text, outputs=gr.HighlightedText())
+
+    with gr.Tab("Personal Information Identifier"):
+        pii_input = gr.Textbox(label="Enter text to analyze")
+        pii_output = gr.Textbox(label="Detected Personal Info")
+        gr.Button("Detect").click(fn=detect_personal_info, inputs=pii_input, outputs=pii_output)
+
+    with gr.Tab("Customer Churn"):
+        churn_inputs = [
+            gr.Radio(['Yes', 'No'], label="SeniorCitizen"),
+            gr.Radio(['Yes', 'No'], label="Partner"),
+            gr.Radio(['No', 'Yes'], label="Dependents"),
+            gr.Slider(1, 73, step=1, label="Tenure (Months)"),
+            gr.Radio(['DSL', 'Fiber optic', 'No Internet'], label="InternetService"),
+            gr.Radio(['No', 'Yes'], label="OnlineSecurity"),
+            gr.Radio(['No', 'Yes'], label="OnlineBackup"),
+            gr.Radio(['No', 'Yes'], label="DeviceProtection"),
+            gr.Radio(['No', 'Yes'], label="TechSupport"),
+            gr.Radio(['No', 'Yes'], label="StreamingTV"),
+            gr.Radio(['No', 'Yes'], label="StreamingMovies"),
+            gr.Radio(['Month-to-month', 'One year', 'Two year'], label="Contract"),
+            gr.Radio(['Yes', 'No'], label="PaperlessBilling"),
+            gr.Radio(['Electronic check', 'Mailed check', 'Bank transfer (automatic)', 'Credit card (automatic)'], label="PaymentMethod"),
+            gr.Slider(18.40, 118.65, label="MonthlyCharges")
+        ]
+        churn_output = gr.Label(label="Churn Prediction")
+        gr.Button("Predict").click(fn=predict_churn, inputs=churn_inputs, outputs=churn_output)
 
-# เรียกใช้งานแอป
 app.launch()
+'''
+
+with open("/mnt/data/app.py", "w") as f:
+    f.write(app_py_content)
+
+"/mnt/data/app.py"